title: Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training.
abstract: This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters presented on a 100 Hz CRT-monitor, three scalp electrodes for signal acquisition, a gUSB-amp for preamplification and two PCs for data-processing and stimulus control respectively. Preliminary test results of the system on nine healthy subjects, with and without tri-training, indicates that the accuracy improves as a result of tri-training.
authors.forename: Thomas
authors.initials: T
authors.lastname: Bender
authors.forename: Troels W
authors.initials: TW
authors.lastname: Kjaer
authors.forename: Carsten E
authors.initials: CE
authors.lastname: Thomsen
authors.forename: Helge B D
authors.initials: HB
authors.lastname: Sorensen
authors.forename: S
authors.initials: S
authors.lastname: Puthusserypady
mesh_terms: D000221:Adaptation, Ocular; D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D062207:Brain-Computer Interfaces; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D055815:Young Adult